ML Infrastructure Expert - Fully Remote

Mercor; San Francisco (CA); Remote; USD 140,000 - 210,000; Full time

Job summary

Mercor is seeking engineers to join its cutting-edge GenAI team and help build foundational AI models and LLM infrastructure. You will work across accelerator kernel programming, performance profiling, and high-throughput inference serving.

This fully remote, 40-hour-per-week role emphasizes hands-on ML systems work, with production experience in JAX or PyTorch and a focus on reliability, scalability, and rigorous evaluation of training data for frontier AI systems.

Qualifications

• 2+ years hands-on experience in ML systems, ML infrastructure, or accelerator performance engineering.
• Experience with at least two kernel, profiling, or serving areas listed above.
• Production experience with JAX and/or PyTorch and knowledge of distributed training or compiler work is a plus.
• Ability to engage reliably for 40 hours per week during weekdays.

Responsibilities

• Design tasks across kernels, profiling, debugging and inference serving, including challenging adversarial problems.
• Guide research and engineering teams to improve AI model performance on ML systems and training infra.
• Evaluate ML system tasks and provide clear technical feedback for expert review.
• Develop guidelines and rubrics for kernel optimization, profiler interpretation, and serving throughput/latency.
• Collaborate with subject matter experts to ensure training data consistency.

Skills

Custom kernels

Performance profiling

Distributed workloads

LLM serving at scale

JAX production experience

PyTorch production experience

XLA/StableHLO/MLIR

Tools

JAX

PyTorch

Kineto

Nsight

TensorRT-LLM

Job description

Join a leading AI lab's cutting-edge GenAI team to be at the core of the AI revolution, where your expertise fuels the development of the most advanced Large Language Models.

1. Overview

Join a leading AI lab's cutting-edge GenAI team and help build foundational AI models from the ground up. We're seeking MLOps and ML systems engineers with hands-on experience in large language model infrastructure across any of four areas: accelerator kernel programming, performance profiling and trace analysis, debugging accelerated and distributed workloads, and high-throughput inference serving. Experience with JAX, XLA or TPUs is especially valuable. This role involves AI model training and evaluation work, including writing and assessing ML systems tasks and solutions to generate high-quality training data for frontier AI systems.

This is a fully remote role open to candidates worldwide. Depending on where you are based, your engagement will be arranged through Cincinnatus LLC or one of its trusted employer-of-record partners in your country, with the opportunity to be placed at a leading AI lab as part of their extended workforce. This is a 40-hour full-time engagement, with no conflicts/no other engagements.

2. Key Responsibilities

• Design challenging tasks across kernels, profiling, debugging and inference serving, including adversarial problems that expose where frontier models fail on accelerator code (for example Pallas kernels on TPU), and write accurate, well-structured solutions to them.

• Guide research and engineering teams to close knowledge gaps and improve AI model performance on ML systems, compilers and training infrastructure topics.

• Evaluate ML systems tasks and solutions, and provide clear written technical feedback that stands up to expert review.

• Develop guidelines and detailed rubrics covering kernel optimization, profiler output interpretation, distributed systems reasoning, and serving throughput and latency trade-offs.

• Collaborate with other subject matter experts to keep training data consistent and accurate.

3. Core Qualifications

• 2+ years of hands-on professional experience in ML systems, ML infrastructure, model serving, or accelerator performance engineering. This is a hands-on systems role rather than an applied modelling or data science one.

• Practical experience in at least one of the following, with more than one a strong plus: writing or optimizing custom kernels (Pallas, Triton, CUDA, CUTLASS); performance profiling and trace analysis (JAX/XLA profiler, Kineto, torch.profiler, Nsight); debugging distributed or accelerator-bound workloads; serving large language models at scale (vLLM, SGLang, TensorRT-LLM, JetStream, KV cache, paged attention, continuous batching).

• Working production experience with JAX and/or PyTorch. Framework-level depth is a strong plus: custom operators, sharding and distributed training (pjit/shard_map, FSDP, Megatron, MaxText), or compiler and graph-level work (XLA/StableHLO, torch.compile, MLIR).

• Experience with modern accelerators such as TPU (v4 to v6e), H100, B200, Trainium or MI300, and the ability to reason about throughput, latency and memory trade-offs.

• Demonstrable career progression.

• Ability to engage reliably for at least 40 hours/week during weekdays.

• Strong written communication skills and the ability to explain complex technical decisions clearly.

Equal Employment Opportunity: Cincinnati is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or any other legally protected characteristic.